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Chemical vapour deposition enables large-domain growth of ideal graphene, yet many applications of graphene require the controlled inclusion of specific defects. We present a one-step chemical vapour deposition procedure aimed at retaining the precursor topology when incorporated into the grown carbonaceous film. When azupyrene, the molecular analogue of the Stone-Wales defect in graphene, is used as a precursor, carbonaceous monolayers with a range of morphologies are produced as a function of the copper substrate growth temperature. The higher the substrate temperature during deposition, the closer the resulting monolayer is to ideal graphene. Analysis, with a set of complementary materials characterisation techniques, reveals morphological changes closely correlated with changes in the atomic adsorption heights, network topology, and concentration of 5-/7-membered carbon rings. The engineered defective carbon monolayers can be transferred to different substrates, potentially enabling applications in nanoelectronics, sensorics, and catalysis., Comment: manuscript and SI provided

We introduce a novel method for obtaining a wide variety of moments of a random variable with a well-defined moment-generating function (MGF). We derive new expressions for fractional moments and fractional absolute moments, both central and non-central moments. The new moment expressions are relatively simple integrals that involve the MGF, but do not require its derivatives. We label the new method CMGF because it uses a complex extension of the MGF and can be used to obtain complex moments. We illustrate the new method with three applications where the MGF is available in closed-form, while the corresponding densities and the derivatives of the MGF are either unavailable or very difficult to obtain.

Rate-matching of low-density parity-check (LDPC) codes enables a single code description to support a wide range of code lengths and rates. In 5G NR, rate matching is accomplished by extending (lifting) a base code to a desired target length and by puncturing (not transmitting) certain code bits. LDPC codes and rate matching are typically designed for the asymptotic performance limit with an ideal decoder. Practical LDPC decoders, however, carry out tens or fewer message-passing decoding iterations to achieve the target throughput and latency of modern wireless systems. We show that one can optimize LDPC code puncturing patterns for such few-iteration-constrained decoders using a method we call swapping of punctured and transmitted blocks (SPAT). Our simulation results show that SPAT yields from 0.20 dB up to 0.55 dB improved signal-to-noise ratio performance compared to the standard 5G NR LDPC code puncturing pattern for a wide range of code lengths and rates., Comment: Accepted at the Asilomar Conference on Signals, Systems, and Computers 2024

The assumptions of stationarity and isotropy often stated over spatial processes have not aged well during the last two decades, partly explained by the combination of computational developments and the increasing availability of high-resolution spatial data. While a plethora of approaches have been developed to relax these assumptions, it is often a costly tradeoff between flexibility and a diversity of computational challenges. In this paper, we present a class of covariance functions that relies on fixed, observable spatial information that provides a convenient tradeoff while offering an extra layer of numerical and visual representation of the flexible spatial dependencies. This model allows for separate parametric structures for different sources of nonstationarity, such as marginal standard deviation, geometric anisotropy, and smoothness. It simplifies to a Mat\'ern covariance function in its basic form and is adaptable for large datasets, enhancing flexibility and computational efficiency. We analyze the capabilities of the presented model through simulation studies and an application to Swiss precipitation data., Comment: 24 pages, 12 figures

We present deep ($\sim$ 20 hr), high-angular resolution Atacama Large Millimeter/submillimeter Array (ALMA) observations of the $\rm CO ~ (4-3)$ and $\rm [CI] ~ (1-0)$ transitions, along with the rest-frame 630 $\mu$m dust continuum, in BX610 --a massive, main-sequence galaxy at the peak epoch of cosmic star formation $(z = 2.21)$. Combined with deep Very Large Telescope (VLT) SINFONI observations of the H$\alpha$ line, we characterize the molecular gas and star formation activity on kiloparsec scales. Our analysis reveals that the excitation of the molecular gas, as traced by the $L'_{\rm CO ~ (4-3)} / L'_{\rm [CI] ~ (1-0)}$ line luminosity ratio, decreases with increasing galactocentric radius. While the line luminosity ratios in the outskirts are similar to those typically found in main-sequence galaxies at $z \sim 1$, the ratios in the central regions of BX610 are comparable to those observed in local starbursts. There is also a giant extra-nuclear star-forming clump in the southwest of BX610 that exhibits high star formation activity, molecular gas abundance, and molecular gas excitation. Furthermore, the central region of BX610 is rich in molecular gas $(M_{\rm mol} / M_{\rm \star} \approx 1)$; however, at the current level of star formation activity, such molecular gas is expected to be depleted in $\sim$ 450 Myr. This, along with recent evidence for rapid inflow toward the center, suggests that BX610 may be experiencing an evolutionary phase often referred to as wet compaction, which is expected to lead to central gas depletion and subsequent inside-out quenching of star formation activity., Comment: Submitted to Astronomy & Astrophysics. 09 pages, 05 figures

Efficient physics simulations are essential for numerous applications, ranging from realistic cloth animations or smoke effects in video games, to analyzing pollutant dispersion in environmental sciences, to calculating vehicle drag coefficients in engineering applications. Unfortunately, analytical solutions to the underlying physical equations are rarely available, and numerical solutions require high computational resources. Latest developments in the field of physics-based Deep Learning have led to promising efficiency improvements but still suffer from limited generalization capabilities and low accuracy compared to numerical solvers. In this work, we introduce Metamizer, a novel neural optimizer that iteratively solves a wide range of physical systems with high accuracy by minimizing a physics-based loss function. To this end, our approach leverages a scale-invariant architecture that enhances gradient descent updates to accelerate convergence. Since the neural network itself acts as an optimizer, training this neural optimizer falls into the category of meta-optimization approaches. We demonstrate that Metamizer achieves unprecedented accuracy for deep learning based approaches - sometimes approaching machine precision - across multiple PDEs after training on the Laplace, advection-diffusion and incompressible Navier-Stokes equation as well as on cloth simulations. Remarkably, the model also generalizes to PDEs that were not covered during training such as the Poisson, wave and Burgers equation. Our results suggest that Metamizer could have a profound impact on future numerical solvers, paving the way for fast and accurate neural physics simulations without the need for retraining.

Nonadiabatic coupling between electrons and molecular motion at metal surfaces leads to energy dissipation and dynamical steering effects during chemical surface dynamics. We present a theoretical approach to the scattering of molecules from metal surfaces that incorporates all nonadiabatic and quantum nuclear effects due to the coupling of the molecular degrees of freedom to the electrons in the metal. This is achieved with the hierarchical equations of motion (HEOM) approach combined with a matrix product state representation in twin space. The method is applied to the scattering of nitric oxide from Au(111), for which strongly nonadiabatic energy loss during scattering has been experimentally observed, thus presenting a significant theoretical challenge. Since the HEOM approach treats the molecule-surface coupling exactly, it captures the interplay between nonadiabatic and quantum nuclear effects. Finally, the data obtained by the HEOM approach is used as a rigorous benchmark to assess various mixed quantum-classical methods, from which we derive insights into the mechanisms of energy dissipation and the suitable working regimes of each method.

This study addresses the challenges of analyzing temporal discrepancies in large language models (LLMs) trained on data from different time periods. To facilitate the automatic exploration of these differences, we propose a novel system that compares in a systematic way the outputs of two LLM versions based on user-defined queries. The system first generates a hierarchical topic structure rooted in a user-specified keyword, allowing for an organized comparison of topical categories. Subsequently, it evaluates the generated text by both LLMs to identify differences in vocabulary, information presentation, and underlying themes. This fully automated approach not only streamlines the identification of shifts in public opinion and cultural norms but also enhances our understanding of the adaptability and robustness of machine learning applications in response to temporal changes. By fostering research in continual model adaptation and comparative summarization, this work contributes to the development of more transparent machine learning models capable of capturing the nuances of evolving societal contexts.

The electric dipole strength distribution in $^{58}$Ni between 6 and 20 MeV has been determined from proton inelastic scattering experiments at very forward angles at RCNP, Osaka. The experimental data are rather well reproduced by quasiparticle random-phase approximation calculations including vibration coupling, despite a mild dependence on the adopted Skyrme interaction. They allow an estimate of the experimentally inaccessible high-energy contribution above 20 MeV, leading to an electric dipole polarizability $\alpha_\mathrm{D}(^{58}{\rm Ni}) = 3.48(31)$ fm$^3$. This serves as a test case for recent extensions of coupled-cluster calculations with chiral effective field theory interactions to nuclei with two nucleons on top of a closed-shell system., Comment: 8 pages, 4 figures

To search for a conceptual picture describing the magnetization plateau phenomenon, we surveyed the crystal structures and the spin lattices of those magnets exhibiting plateaus in their magnetization vs. magnetic field curves by probing the three questions: (a) why only certain magnets exhibit magnetization plateaus, (b) why there occur several different types of magnetization plateaus, and (c) what controls the widths of magnetization plateaus. We show that the answers to these questions lie in how the magnets under field absorb Zeeman energy hence changing their magnetic structures. The magnetic structure of a magnet insulator is commonly described in terms of its spin lattice, which requires the determination of the spin exchanges nonnegligible strengths between the magnetic ions. Our work strongly suggests that a magnet under magnetic field partitions its spin lattice into antiferromagnetic (AFM) or ferrimagnetic fragments by breaking its weak magnetic bonds. Our supposition of the field-induced partitioning of spin lattice into magnetic fragments is supported by the anisotropic magnetization plateaus of Ising magnets and by the highly anisotropic width of the 1/3-magnetization plateau in azurite. The answers to the three questions (a) - (c) emerge naturally by analyzing how these fragments are formed under magnetic field., Comment: A survey of the magnetization plateau phenomenon; 128 pages including the Supporting Information

We develop the theory of local operations and classical communication (LOCC) for bipartite quantum systems represented by commuting von Neumann algebras. Our central result is the extension of Nielsen's Theorem to arbitrary factors. As in the matrix algebra case, the LOCC ordering of bipartite pure states is connected to the majorization of their restrictions. Our theorem implies that, in a bipartite system modeled by commuting factors in Haag duality, a) all states have infinite one-shot entanglement if and only if the local factors are not of type I, b) type III factors are characterized by LOCC transitions of arbitrary precision between any two pure states, and c) the latter holds even without classical communication for type III$_{1}$ factors. In the case of semifinite factors, the usual construction of entanglement monotones carries over using majorization theory. In the appendix, we provide a self-contained treatment of majorization on semifinite von Neumann algebras and $\sigma$-finite measure spaces., Comment: 35+13+5 pages, 1 figure

The paper considers the time periodic problem of the Navier-Stokes system in an exterior domain under time periodic external forces. Existence of periodic mild solutions is obtained in the critical scale invariant space $C(\mathbb{R};L^n)$ if the external force is small without exploiting any divergence form as in the study of Okabe and Tsutsui (2017) for the whole space case in Lorentz spaces. Previous studies mainly rely on either potential theoretical estimates or time-space integral estimates in Lorentz spaces introduced by Yamazaki (Math. Ann.(2000)). To the best of our knowledge, there are no results using Muckenhoupt weights in $L^q$ class for $1< q <\infty$ to construct time periodic solutions of the Navier-Stokes equations in the exterior domain case. In this article, a new method based on radially symmetric Muckenhoupt weights in space is used. To apply these weights, we reconsider weighted $L^p$-$L^q$ decay estimates for the Stokes semigroup. This important result was announced by Kobayashi and Kubo (2012-2015) about ten years ago with a sketch of the proof by Kubo. In this paper, we give a rigorous proof of the result and, as an important application, solve the time periodic problem for the Navier-Stokes equations on an exterior domain., Comment: 32 pages

Electrodeposition is a fundamental process in electrochemistry, and has applications in numerous industries, such as corrosion protection, decorative finishing, energy storage, catalysis, and electronics. While there is a long history of using electrodeposition, its application for controlled nanostructure growth is limited. The establishment of an atomic-scale understanding of the electrodeposition process and dynamics is crucial to enable the controlled fabrication of metal nanoparticles and other nanostructures. Significant advancements in molecular simulation capabilities and the electronic structure theory of electrified solid-liquid interfaces bring theory closer to realistic applications, but a gap remains between realistic applications, theoretical understanding of dynamics, and atomistic simulation. In this review we briefly summarize the current state-of-the-art computational techniques available for the simulation of electrodeposition and electrochemical growth on surfaces, and identify the remaining open challenges., Comment: 72 pages, 6 figures

This article introduces the Clarke transform and Clarke coordinates, which present a solution to the disengagement of an arbitrary number of coupled displacement actuation of continuum and soft robots. The Clarke transform utilizes the generalized Clarke transformation and its inverse to reduce any number of joint values to a two-dimensional space without sacrificing any significant information. This space is the manifold of the joint space and is described by two orthogonal Clarke coordinates. Application to kinematics, sampling, and control are presented. By deriving the solution to the previously unknown forward robot-dependent mapping for an arbitrary number of joints, the forward and inverse kinematics formulations are branchless, closed-form, and singular-free. Sampling is used as a proxy for gauging the performance implications for various methods and frameworks, leading to a branchless, closed-form, and vectorizable sampling method with a 100 percent success rate and the possibility to shape desired distributions. Due to the utilization of the manifold, the fairly simple constraint-informed, two-dimensional, and linear controller always provides feasible control outputs. On top of that, the relations to improved representations in continuum and soft robotics are established, where the Clarke coordinates are their generalizations. The Clarke transform offers valuable geometric insights and paves the way for developing approaches directly on the two-dimensional manifold within the high-dimensional joint space, ensuring compliance with the constraint. While being an easy-to-construct linear map, the proposed Clarke transform is mathematically consistent, physically meaningful, as well as interpretable and contributes to the unification of frameworks across continuum and soft robots., Comment: 27 pages, 11 figures, 5 tables

Objective: To propose a mathematical model for applying Ionization Detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP). Approach: Our model provides for selection of preferred ID parameters (I_p) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopic I_p and macroscopic RTP. Selection of I_p is demonstrated using published cell survival measurements for protons through argon, comparing results for nineteen Ip: N_k; k = 2,3,...,10, the number of ionizations in clusters of k or more per particle, and F_k; k = 1,2,...,10, the number of clusters of k or more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation. Main results: The preferred I_p were N_4 and F_5 for aerobic cells, N_5 and F_7 for hypoxic cells. Signifcant differences were found in cell survival for beams having the same LET or the preferred N_k. Conversely, there was no signi?cant difference for F_5 for aerobic cells and F_7 for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for these I_p had the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert that I_p exist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation. Signifcance: Our model provides a practical means to select preferred I_p from radiobiological data, and to convert I_p to the macroscopic cluster dose for particle RTP., Comment: 31 pages, 12 figures

For almost all tendon-driven continuum robots, a segment is actuated by three or four tendons constrained by its mechanical design. For both cases, methods to account for the constraints are known. However, for an arbitrary number of tendons, a disentanglement method has yet to be formulated. Motivated by this unsolved general case, we explored state representations and exploited the two-dimensional manifold. We found that the Clarke transformation, a mathematical transformation used in vector control, can be generalized to address this problem. We present the Clarke transform and Clarke coordinates, which can be used to overcome the troublesome interdependency between the tendons, simplify modeling, and unify different improved state representations. Further connection to arc parameters leads to the possibility to derive more generalizable approaches applicable to a wider range of robot types., Comment: Accepted for presentation at 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40). 4 pages, 3 figures, cheat sheet

We generalize the targeted $\mathcal{B}$-statistic for continuous gravitational waves by modeling the $h_0$-prior as a half-Gaussian distribution with scale parameter $H$. This approach retains analytic tractability for two of the four amplitude marginalization integrals and recovers the standard $\mathcal{B}$-statistic in the strong-signal limit ($H\rightarrow\infty$). By Taylor-expanding the weak-signal regime ($H\rightarrow0$), the new prior enables fully analytic amplitude marginalization, resulting in a simple, explicit statistic that is as computationally efficient as the maximum-likelihood $\mathcal{F}$-statistic, but significantly more robust. Numerical tests show that for day-long coherent searches, the weak-signal Bayes factor achieves sensitivities comparable to the $\mathcal{F}$-statistic, though marginally lower than the standard $\mathcal{B}$-statistic (and the Bero-Whelan approximation). In semi-coherent searches over short (compared to a day) segments, this approximation matches or outperforms the weighted dominant-response $\mathcal{F}_{\mathrm{ABw}}$-statistic and returns to the sensitivity of the (weighted) $\mathcal{F}_{\mathrm{w}}$-statistic for longer segments. Overall the new Bayes-factor approximation demonstrates state-of-the-art or improved sensitivity across a wide range of segment lengths we tested (from 900s to 10days)., Comment: 25 pages, 4 figures, to be submitted to CQG

We resolve the multiple images of the binary-lens microlensing event ASASSN-22av using the GRAVITY instrument of the Very Large Telescope Interferometer (VLTI). The light curves show weak binary perturbations, complicating the analysis, but the joint modeling with the VLTI data breaks several degeneracies, arriving at a strongly favored solution. Thanks to precise measurements of angular Einstein radius \theta_E = 0.724 +/- 0.002 mas and microlens parallax, we determine that the lens system consists of two M dwarfs with masses of M_1 = 0.258 +/- 0.008 M_sun and M_2 = 0.130 +/- 0.007 M_sun, a projected separation of r_\perp = 6.83 +/- 0.31 AU and a distance of D_L = 2.29 +/- 0.08 kpc. The successful VLTI observations of ASASSN-22av open up a new path for studying intermediate-separation (i.e., a few AUs) stellar-mass binaries, including those containing dark compact objects such as neutron stars and stellar-mass black holes., Comment: Accepted by ApJ. See the ancillary file for animation associated with Fig. 8

Studying the orbital motion of stars around Sagittarius A* in the Galactic Center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2, as predicted by General Relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately $10 \sigma$ confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprised of old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately $1200 \, M_\odot$ with a $1 \sigma$ confidence level. This significantly improves our constraints on the mass distribution in the Galactic Center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic Center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*., Comment: Submitted to A&A on September 17, 2024

A major challenge of the long measurement times in magnetic resonance imaging (MRI), an important medical imaging technology, is that patients may move during data acquisition. This leads to severe motion artifacts in the reconstructed images and volumes. In this paper, we propose a deep learning-based test-time-training method for accurate motion estimation. The key idea is that a neural network trained for motion-free reconstruction has a small loss if there is no motion, thus optimizing over motion parameters passed through the reconstruction network enables accurate estimation of motion. The estimated motion parameters enable to correct for the motion and to reconstruct accurate motion-corrected images. Our method uses 2D reconstruction networks to estimate rigid motion in 3D, and constitutes the first deep learning based method for 3D rigid motion estimation towards 3D-motion-corrected MRI. We show that our method can provably reconstruct motion parameters for a simple signal and neural network model. We demonstrate the effectiveness of our method for both retrospectively simulated motion and prospectively collected real motion-corrupted data.

Chemo-mechanical waves on active deformable surfaces are a key component for many vital cellular functions. In particular, these waves play a major role in force generation and long-range signal transmission in cells that dynamically change shape, as encountered during cell division or morphogenesis. Reconstituting and controlling such chemically controlled cell deformations is a crucial but unsolved challenge for the development of synthetic cells. Here, we develop an optogenetic method to elucidate the mechanism responsible for coordinating surface contraction waves that occur in oocytes of the starfish Patiria miniata during meiotic cell division. Using spatiotemporally-patterned light stimuli as a control input, we create chemo-mechanical cortical excitations that are decoupled from meiotic cues and drive diverse shape deformations ranging from local pinching to surface contraction waves and cell lysis. We develop a quantitative model that entails the hierarchy of chemical and mechanical dynamics, which allows to relate the variety of mechanical responses to optogenetic stimuli. Our framework systematically predicts and explains transitions of programmed shape dynamics. Finally, we qualitatively map the observed shape dynamics to elucidate how the versatility of intracellular protein dynamics can give rise to a broad range of mechanical phenomenologies. More broadly, our results pave the way toward real-time control over dynamical deformations in living organisms and can advance the design of synthetic cells and life-like cellular functions., Comment: 36 pages, 16 figures, 11 movies

Planar scanning probe microscopy is a recently emerging alternative approach to tip-based scanning probe imaging. It can scan an extended planar sensor, such as a polished bulk diamond doped with magnetic-field-sensitive nitrogen-vacancy (NV) centers, in nanometer-scale proximity of a planar sample. So far, this technique has been limited to optical near-field microscopy, and has required nanofabrication of the sample of interest. Here we extend this technique to magnetometry using NV centers, and present a modification that removes the need for sample-side nanofabrication. We harness this new ability to perform a hitherto infeasible measurement - direct imaging of the three-dimensional vector magnetic field of magnetic vortices in a thin film magnetic heterostructure, based on repeated scanning with NV centers with different orientations within the same scanning probe. Our result opens the door to quantum sensing using multiple qubits within the same scanning probe, a prerequisite for the use of entanglement-enhanced and massively parallel schemes.

We detail the steps required to deploy a multi-user multiple-input multiple-output (MU-MIMO) neural receiver (NRX) in an actual cellular communication system. This raises several exciting research challenges, including the need for real-time inference and compatibility with the 5G NR standard. As the network configuration in a practical setup can change dynamically within milliseconds, we propose an adaptive NRX architecture capable of supporting dynamic modulation and coding scheme (MCS) configurations without the need for any re-training and without additional inference cost. We optimize the latency of the neural network (NN) architecture to achieve inference times of less than 1ms on an NVIDIA A100 GPU using the TensorRT inference library. These latency constraints effectively limit the size of the NN and we quantify the resulting signal-to-noise ratio (SNR) degradation as less than 0.7 dB when compared to a preliminary non-real-time NRX architecture. Finally, we explore the potential for site-specific adaptation of the receiver by investigating the required size of the training dataset and the number of fine-tuning iterations to optimize the NRX for specific radio environments using a ray tracing-based channel model. The resulting NRX is ready for deployment in a real-time 5G NR system and the source code including the TensorRT experiments is available online.

We study the radial transport of cold gas within simulated disk galaxies at cosmic noon, aiming at distinguishing between disk instability and accretion along cold streams from the cosmic web as its driving mechanism. Disks are selected based on kinematics and flattening from the VELA zoom-in hydro-cosmological simulations. The radial velocity fields in the disks are mapped, their averages are computed as a function of radius and over the whole disk, and the radial mass flux in each disk as a function of radius is obtained. The transport directly associated with fresh incoming streams is identified by selecting cold gas cells that are either on incoming streamlines or have low metallicity. The radial velocity fields in VELA disks are found to be highly non-axisymmetric, showing both inflows and outflows. However, in most cases, the average radial velocities, both as a function of radius and over the whole disk, are directed inwards, with the disk-averaged radial velocities typically amounting to a few percent of the disk-averaged rotational velocities. This is significantly lower than the expectations from various models that analytically predict the inward mass transport as driven by torques associated with disk instability. Under certain simplifying assumptions, the latter typically predict average inflows of more than $10\%$ of the rotational velocities. Analyzing the radial motions of streams and off-stream material, we find that the radial inflow in VELA disks is dominated by the stream inflows themselves, especially in the outer disks. The high inward radial velocities inferred in observed disks at cosmic noon, at the level of $\sim \! 20\%$ of the rotational velocities, may reflect inflowing streams from the cosmic web rather than being generated by disk instability., Comment: 20 pages, 14 figures, submitted to A&A

We perform a search for continuous nearly monochromatic gravitational waves from the central compact objects associated with the supernova remnants Vela Jr. and G347.3 using LIGO O2 and O3 public data. Over $10^{18}$ different waveforms are considered, covering signal frequencies between 20-1300 Hz (20-400 Hz) for G347.3-0.5 (Vela Jr) and a very broad range of frequency derivatives. Thousands of volunteers donating compute cycles through the computing project Einstein@Home have made this endeavour possible. Following the Einstein@Home search, we perform multi-stage follow-ups of over 5 million waveforms. The selection threshold is set so that a signal could be confirmed using the first half of the LIGO O3 data. We find no significant signal candidate for either targets. Based on this null result, for G347.3-0.5, we set the most constraining upper limits to date on the amplitude of gravitational wave signals, corresponding to deformations below $10^{-6}$ in a large part of the search band. At the frequency of best strain sensitivity, near $161$ Hz, we set 90\%\ confidence upper limits on the gravitational wave intrinsic amplitude of $h_0^{90\%}\approx 6.2\times10^{-26}$. Over most of the frequency range our upper limits are a factor of 10 smaller than the indirect age-based upper limit. For Vela Jr., near $163$ Hz, we set $h_0^{90\%}\approx 6.4\times10^{-26}$. Over most of the frequency range our upper limits are a factor of 15 smaller than the indirect age-based upper limit. The Vela Jr. upper limits presented here are slightly less constraining than the most recent upper limits of \cite{ligo_o3a_c_v} but they apply to a broader set of signals.

Many real-world materials are characterized by a glittery appearance. Reproducing this effect in physically based renderings is a challenging problem due to its discrete nature, especially in real-time applications which require a consistently low runtime. Recent work focuses on glittery appearance illuminated by infinitesimally small light sources only. For light sources like the sun this approximation is a reasonable choice. In the real world however, all light sources are fundamentally area light sources. In this paper, we derive an efficient method for rendering glints illuminated by spatially constant diffuse area lights in real time. To this end, we require an adequate estimate for the probability of a single microfacet to be correctly oriented for reflection from the source to the observer. A good estimate is achieved either using linearly transformed cosines (LTC) for large light sources, or a locally constant approximation of the normal distribution for small spherical caps of light directions. To compute the resulting number of reflecting microfacets, we employ a counting model based on the binomial distribution. In the evaluation, we demonstrate the visual accuracy of our approach, which is easily integrated into existing real-time rendering frameworks, especially if they already implement shading for area lights using LTCs and a counting model for glint shading under point and directional illumination. Besides the overhead of the preexisting constituents, our method adds little to no additional overhead.

In the dynamics of atoms and molecules at metal surfaces, electron-hole pair excitations can play a crucial role. In the case of hyperthermal hydrogen atom scattering, they lead to nonadiabatic energy loss and highly inelastic scattering. Molecular dynamics with electronic friction simulations where friction is computed under an isotropic homogeneous electron gas approximation have previously shown good agreement with measured kinetic energy loss distributions, suggesting that this level of theoretical description is sufficient to describe nonadiabatic effects of atomic scattering. In this work, we show that similar agreement with room temperature experiments can also be achieved with friction derived from density functional theory linear response calculations. The apparent agreement of the homogeneous electron gas approximation with experiment arises from a fortuitous cancellation of errors where friction is overestimated close to the surface and the spin transition is neglected. Only for scattering at low temperatures can both approximations be distinguished and differences rationalised in terms of the number of bounces of the atom on the surface. We identify the signatures of nonadiabatic energy loss of different levels of theory, which future low-temperature scattering experiments will be able to measure., Comment: 30 pages, 8 figures, includes supplemental material

The sensitivity of wide-parameter-space searches for continuous gravitational waves (CWs) is limited by their high computational cost. Deep learning is being studied as an alternative method to replace various aspects of a CW search. In previous work arXiv:2305.01057[gr-qc], new design principles were presented for deep neural network (DNN) search of CWs and such DNNs were trained to perform a targeted search with matched filtering sensitivity. In this paper, we adapt these design principles to build a DNN architecture for wide parameter-space searches in ten days of data from two detectors (H1 and L1). We train a DNN for each of the benchmark cases: six all-sky searches and eight directed searches at different frequencies in the search band of 20 - 1000 Hz. We compare our results to the DNN sensitivity achieved from arXiv:2005.04140[gr-qc] and find that our trained DNNs are more sensitive in all the cases. The absolute improvement in detection probability ranges from 6.5% at 20 Hz to 38% at 1000 Hz in the all-sky cases and from 1.5% at 20 Hz to 59.4% at 500 Hz in the directed cases. An all-sky DNN trained on the entire search band of 20 - 1000 Hz shows a high sensitivity at all frequencies providing a proof of concept for training a single DNN to perform the entire search. We also study the generalization of the DNN performance to signals with different signal amplitude, frequency and the dependence of the DNN sensitivity on sky-position., Comment: 10 pages, 4 figures

We consider the quantum dynamics of a particle on a lattice for large times. Assuming translation invariance, and either discrete or continuous time parameter, the distribution of the ballistically scaled position $Q(t)/t$ converges weakly to a distribution that is compactly supported in velocity space, essentially the distribution of group velocity in the initial state. We show that the total probability of velocities strictly outside the support of the asymptotic measure goes to zero exponentially with $t$, and we provide a simple method to estimate the exponential rate uniformly in the initial state. Near the boundary of the allowed region the rate function goes to zero like the power 3/2 of the distance to the boundary. The method is illustrated in several examples., Comment: 20 pages, 6 figures

The set of answers to a query may be very large, potentially overwhelming users when presented with the entire set. In such cases, presenting only a small subset of the answers to the user may be preferable. A natural requirement for this subset is that it should be as diverse as possible to reflect the variety of the entire population. To achieve this, the diversity of a subset is measured using a metric that determines how different two solutions are and a diversity function that extends this metric from pairs to sets. In the past, several studies have shown that finding a diverse subset from an explicitly given set is intractable even for simple metrics (like Hamming distance) and simple diversity functions (like summing all pairwise distances). This complexity barrier becomes even more challenging when trying to output a diverse subset from a set that is only implicitly given such as the query answers of a query and a database. Until now, tractable cases have been found only for restricted problems and particular diversity functions. To overcome these limitations, we focus on the notion of ultrametrics, which have been widely studied and used in many applications. Starting from any ultrametric $d$ and a diversity function $\delta$ extending $d$, we provide sufficient conditions over $\delta$ for having polynomial-time algorithms to construct diverse answers. To the best of our knowledge, these conditions are satisfied by all diversity functions considered in the literature. Moreover, we complement these results with lower bounds that show specific cases when these conditions are not satisfied and finding diverse subsets becomes intractable. We conclude by applying these results to the evaluation of conjunctive queries, demonstrating efficient algorithms for finding a diverse subset of solutions for acyclic conjunctive queries when the attribute order is used to measure diversity.

Time-Sensitive Networking (TSN) enables the transmission of multiple traffic types within a single network. While the performance of high-priority traffic has been extensively studied in recent years, the performance of low-priority traffic varies significantly between different TSN forwarding algorithms. This paper provides an overview of existing TSN forwarding algorithms and discusses their impact on best-effort traffic. The effects are quantified through simulations of synthetic and realistic networks. The considered forwarding mechanisms are Strict Priority (SP), Asynchronous Traffic Shaper (ATS), Credit-Based Shaper (CBS), Enhanced Transmission Selection (ETS), and Time-Aware Shaper (TAS). The findings indicate that ATS, CBS, and ETS can significantly reduce queuing delays and queue lengths for best-effort traffic when compared to SP and TAS. This effect is enhanced when the reserved bandwidth for high priority queues - using CBS, ATS, or ETS - is reduced to the lowest possible value, within the reserved rate and latency requirements. Specifically, the simulations demonstrate that the choice of forwarding algorithm can improve the performance of low-priority traffic by up to twenty times compared to the least effective algorithm. This study not only provides a comprehensive understanding of the various TSN forwarding algorithms but can also serve as guidance at networks' design time to improve the performance for all types of traffic., Comment: The definitive Version of Record was accepted at the 12th International Conference on Computer and Communications Management (ICCCM 2024), July 19-21, 2024, Kagoshima-shi, Japan [in press]

We introduce Reality Fusion, a novel robot teleoperation system that localizes, streams, projects, and merges a typical onboard depth sensor with a photorealistic, high resolution, high framerate, and wide field of view (FoV) rendering of the complex remote environment represented as 3D Gaussian splats (3DGS). Our framework enables robust egocentric and exocentric robot teleoperation in immersive VR, with the 3DGS effectively extending spatial information of a depth sensor with limited FoV and balancing the trade-off between data streaming costs and data visual quality. We evaluated our framework through a user study with 24 participants, which revealed that Reality Fusion leads to significantly better user performance, situation awareness, and user preferences. To support further research and development, we provide an open-source implementation with an easy-to-replicate custom-made telepresence robot, a high-performance virtual reality 3DGS renderer, and an immersive robot control package. (Source code: https://github.com/uhhhci/RealityFusion), Comment: Accepted, to appear at IROS 2024

Polar ice develops anisotropic crystal orientation fabrics under deformation, yet ice is most often modelled as an isotropic fluid. We present three-dimensional simulations of the crystal orientation fabric of Derwael Ice Rise including the surrounding ice shelf using a crystal orientation tensor evolution equation corresponding to a fixed velocity field. We use a semi-Lagrangian numerical method that constrains the degree of crystal orientation evolution to solve the equations in complex flow areas. We perform four simulations based on previous studies, altering the rate of evolution of the crystal anisotropy and its dependence on a combination of the strain rate and deviatoric stress tensors. We provide a framework for comparison with radar observations of the anisotropy field, outlining areas where the assumption of one vertical eigenvector may not hold and provide resulting errors in measured eigenvalues. We recognise the areas of high horizontal divergence at the ends of the flow divide as important areas to make comparisons with observations. Here, poorly constrained model parameters result in the largest difference in fabric type. These results are important in the planning of future campaigns for gathering data to constrain model parameters and as a link between observations and computationally-efficient, simplified models of anisotropy.

The study of cycloaddition mechanisms is central to the fabrication of extended sp2 carbon nanostructures. Reaction modeling in this context has focused mostly on putative, energetically preferred, exothermic products with limited consideration for symmetry allowed or forbidden mechanistic effects. Here, we introduce a scheme for classifying symmetry-forbidden reaction coordinates in Woodward-Hoffmann correlation diagrams. Topological classifiers grant access to the study of reaction pathways and correlation diagrams in the same footing, for the purpose of elucidating mechanisms and products of polycyclic aromatic azomethine ylide (PAMY) cycloadditions with pentacene-yielding polycyclic aromatic hydrocarbons with an isoindole core in the solid-state and on surfaces as characterized by mass spectrometry and scanning tunneling microscopy, respectively. By means of a tight-binding reaction model and density functional theory (DFT) we find topologically-allowed pathways if a product is endothermic, and topologically-forbidden if a product is exothermic. Our work unveils topological classification as a crucial element for reaction modeling for nanographene engineering, and highlights its fundamental role in the design of cycloadditions in on-surface and solid-state chemical reactions, while underscoring that exothermic pathways can be topologically-forbidden.

Nanographene-based magnetism at interfaces offers an avenue to designer quantum materials towards novel phases of matter and atomic-scale applications. Key to spintronics applications at the nanoscale is bistable spin-crossover which however remains to be demonstrated in nanographenes. Here we show that antiaromatic 1,4-disubstituted pyrazine-embedded nanographene derivatives, which promote magnetism through oxidation to a non-aromatic radical are prototypical models for the study of carbon-based thermal spin-crossover. Scanning tunneling spectroscopy studies reveal symmetric spin excitation signals which evolve at Tc to a zero-energy peak, and are assigned to the transition of a S = 3/2 high-spin to a S = 1/2 low-spin state by density functional theory. At temperatures below and close to the spin-crossover Tc, the high-spin S= 3/2 excitations evidence pronouncedly different temperature-dependent excitation energies corresponding to a zero-field splitting in the Hubbard-Kanamori Hamiltonian. The discovery of thermal spin crossover and temperature-dependent zero-field splitting in carbon nanomaterials promises to accelerate quantum information, spintronics and thermometry at the atomic scale.

The continuous technological development of electronic devices and the introduction of new materials leads to ever greater demands on the fabrication of semiconductor heterostructures and their characterization. This work focuses on optimizing Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) depth profiles of semiconductor heterostructures aiming at a minimization of measurement-induced profile broadening. As model system, a state-of-the-art Molecular Beam Epitaxy (MBE) grown multilayer homostructure consisting of $^{\textit{nat}}$Si/$^{28}$Si bilayers with only 2 nm in thickness is investigated while varying the most relevant sputter parameters. Atomic concentration-depth profiles are determined and an error function based description model is used to quantify layer thicknesses as well as profile broadening. The optimization process leads to an excellent resolution of the multilayer homostructure. The results of this optimization guide to a ToF-SIMS analysis of another MBE grown heterostructure consisting of a strained and highly purified $^{28}$Si layer sandwiched between two Si$_{0.7}$Ge$_{0.3}$ layers. The sandwiched $^{28}$Si layer represents a quantum well that has proven to be an excellent host for the implementation of electron-spin qubits., Comment: 10 pages, 10 figures

The perovskite oxide EuTiO3 (ETO) has attracted increased scientific interest due to its potential multiferroic properties and magnetic activity above and below its structural phase transition at TS=282K. Various experiments have indirectly evidenced that this transition is neither a cubic tetragonal nor the only one occurring in ETO. Here, we show new results demonstrating two further instabilities below TS based on lattice dynamics and spin-phonon interactions combined with a Landau free energy model with coupled order parameters. The new transition temperatures perfectly agree with available experimental data where further instabilities have been anticipated., Comment: 14 pages, 5 figures

We study the time evolution of a one-dimensional system of strongly correlated electrons (a 'sample') that is suddenly coupled to a smaller, initially empty system (a 'nanoprobe'), which can subsequently move along the system. Our purpose here is to study the role of interactions in this model system when it is far from equilibrium. We therefore take both the sample and the nanoprobe to be described by a Hubbard model with on-site repulsive interactions and nearest-neighbor hopping. We compute the behavior of the local particle density and the local density of states (LDOS) as a function of time using time-dependent matrix product states at quarter and at half filling, fillings at which the chain realizes a Luttinger liquid or a Mott insulator, respectively. This allows us to study in detail the oscillation of the particles between the sample and the nanoprobe. While, for noninteracting systems, the LDOS is time-independent, in the presence of interactions, the backflow of electrons to the sample will lead to nontrivial dynamics in the LDOS. In particular, studying the time-dependent LDOS allows us to study how the Mott gap closes locally and how this melting of the Mott insulator propagates through the system in time after such a local perturbation -- a behavior that we envisage can be investigated in future experiments on ultrashort time scales or on optical lattices using microscopy setups.

Despite extensive research on jamming attacks on wireless communication systems, the potential of machine learning for amplifying the threat of such attacks, or our ability to mitigate them, remains largely untapped. A key obstacle to such research has been the absence of a suitable framework. To resolve this obstacle, we release PyJama, a fully-differentiable open-source library that adds jamming and anti-jamming functionality to NVIDIA Sionna. We demonstrate the utility of PyJama (i) for realistic MIMO simulations by showing examples that involve forward error correction, OFDM waveforms in time and frequency, realistic channel models, and mobility; and (ii) for learning to jam. Specifically, we use stochastic gradient descent to optimize jamming power allocation over an OFDM resource grid. The learned strategies are non-trivial, intelligible, and effective., Comment: Accepted at IEEE SPAWC 2024

Dynamics of coarse-grained particle systems derived via the Mori-Zwanzig projection formalism commonly take the form of a (generalized) Langevin equation with configuration-dependent friction and diffusion tensors. In this article, we introduce a class of equivariant representations of tensor-valued functions based on the Atomic Cluster Expansion (ACE) framework that allows for efficient learning of such configuration-dependent friction and diffusion tensors from data. Besides satisfying the correct equivariance properties with respect to the Euclidean group E(3), the resulting heat bath models satisfy a fluctuation-dissipation relation. Moreover, our models can be extended to include additional symmetries, such as momentum conservation, to preserve the hydrodynamic properties of the particle system. We demonstrate the capabilities of the model by constructing a model of configuration-dependent tensorial electronic friction calculated from first principles that arises during reactive molecular dynamics at metal surfaces.

The inverse design of photonic integrated circuits (PICs) presents distinctive computational challenges, including their large memory requirements. Advancements in the two-photon polymerization (2PP) fabrication process introduce additional complexity, necessitating the development of more flexible optimization algorithms to enable the creation of multi-material 3D structures with unique properties. This paper presents an efficient reverse-mode automatic differentiation framework for finite-difference timedomain (FDTD) simulations that is able to handle several constraints arising from novel fabrication methods. Our method is based on straight-through gradient estimation that enables non-differentiable shape parametrizations. We demonstrate the effectiveness of our approach by creating increasingly complex structures to solve the coupling problem in integrated photonic circuits. The results highlight the potential of our method for future PIC design and practical applications., Comment: 19 pages, 10 figures

We report on the wafer scale fabrication of single mode low-loss lithium niobate on insulator waveguides utilizing a chemically amplified resist and an optimized dry etching method. The fabricated single mode waveguides are free of residuals and re-deposition, with measured losses for straight waveguides around 2 dB/m (0.02 dB/cm). We present on a method offering advantages for large-scale production due to its cost-effectiveness, faster writing time, and simplified processes. This work holds promise for advancing integrated photonics and optical communication technologies.

This paper introduces a novel course design in the Master Program in Biostatistics at the University of Zurich that integrates computing skills, effective communication, reproducibility, and scientific integrity within one course. Utilizing a flipped classroom model, the course aims to equip students with the necessary competencies to handle real-world data analysis challenges and effective statistical practice in general. The curriculum includes practical tools such as version control with Git, dynamic reporting, unit testing and containerization to foster reproducibility, and integrity in statistical practice. Feedback gathered from both staff and students post-implementation indicates that the course significantly enhances student readiness for professional and academic environments, demonstrating the effectiveness of this educational approach.

A systematic study, based on the third-moment structure function, of Sgr A*'s variability finds an exponential rise time $\tau_{1,\rm{obs}}=14.8^{+0.4}_{-1.5}~\mathrm{minutes}$ and decay time $\tau_{2,\rm{obs}}=13.1^{+1.3}_{-1.4}~\mathrm{minutes}$. This symmetry of the flux-density variability is consistent with earlier work, and we interpret it as caused by the dominance of Doppler boosting, as opposed to gravitational lensing, in Sgr~A*'s light curve. A relativistic, semi-physical model of Sgr~A* confirms an inclination angle $i<45$ degrees. The model also shows that the emission of the intrinsic radiative process can have some asymmetry even though the observed emission does not. The third-moment structure function, which is a measure of the skewness of the light-curve increments, may be a useful summary statistic in other contexts of astronomy because it senses only temporal asymmetry, i.e., it averages to zero for any temporally symmetric signal., Comment: Accepted for publication in A&A letters

Database systems are often confronted with queries that join many tables but ultimately only output comparatively small aggregate information. Despite all advances in query optimisation, the explosion of intermediate results as opposed to a much smaller final result challenges modern relational database management systems (DBMSs). In this work, we propose the integration of optimisation techniques into relational DBMSs that aim at minimising, and often entirely eliminating, the need for materialising join results for aggregate queries, provided that they satisfy certain conditions. Apart from novel logical optimisations aimed at practicability, we also provide new, natural, physical operators for combining joins and counting with the aim of reducing the size of intermediate results. We experimentally validate the efficacy of our optimisations through their implementation in Spark SQL, but we note that they are naturally applicable in any RDBMS. Our experiments show consistent significant speed-ups -- often by factor 2 and higher -- for analytical and graph queries. At the same time, we observe no performance degradation, even on queries which, from a theoretical point of view, are least amenable to the proposed optimisations.

Reproducibility is a foundational standard for validating scientific claims in computational research. Stochastic computational models are employed across diverse fields such as systems biology, financial modelling and environmental sciences. Existing infrastructure and software tools support various aspects of reproducible model development, application, and dissemination, but do not adequately address independently reproducing simulation results that form the basis of scientific conclusions. To bridge this gap, we introduce the Empirical Characteristic Function Equality Convergence Test (EFECT), a data-driven method to quantify the reproducibility of stochastic simulation results. EFECT employs empirical characteristic functions to compare reported results with those independently generated by assessing distributional inequality, termed EFECT error, a metric to quantify the likelihood of equality. Additionally, we establish the EFECT convergence point, a metric for determining the required number of simulation runs to achieve an EFECT error value of a priori statistical significance, setting a reproducibility benchmark. EFECT supports all real-valued and bounded results irrespective of the model or method that produced them, and accommodates stochasticity from intrinsic model variability and random sampling of model inputs. We tested EFECT with stochastic differential equations, agent-based models, and Boolean networks, demonstrating its broad applicability and effectiveness. EFECT standardizes stochastic simulation reproducibility, establishing a workflow that guarantees reliable results, supporting a wide range of stakeholders, and thereby enhancing validation of stochastic simulation studies, across a model's lifecycle. To promote future standardization efforts, we are developing open source software library libSSR in diverse programming languages for easy integration of EFECT., Comment: 25 pages, 4 figures